435 research outputs found

    Query processing of spatial objects: Complexity versus Redundancy

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    The management of complex spatial objects in applications, such as geography and cartography, imposes stringent new requirements on spatial database systems, in particular on efficient query processing. As shown before, the performance of spatial query processing can be improved by decomposing complex spatial objects into simple components. Up to now, only decomposition techniques generating a linear number of very simple components, e.g. triangles or trapezoids, have been considered. In this paper, we will investigate the natural trade-off between the complexity of the components and the redundancy, i.e. the number of components, with respect to its effect on efficient query processing. In particular, we present two new decomposition methods generating a better balance between the complexity and the number of components than previously known techniques. We compare these new decomposition methods to the traditional undecomposed representation as well as to the well-known decomposition into convex polygons with respect to their performance in spatial query processing. This comparison points out that for a wide range of query selectivity the new decomposition techniques clearly outperform both the undecomposed representation and the convex decomposition method. More important than the absolute gain in performance by a factor of up to an order of magnitude is the robust performance of our new decomposition techniques over the whole range of query selectivity

    Moving Beyond Noninformative Priors: Why and How to Choose Weakly Informative Priors in Bayesian Analyses

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    Throughout the last two decades, Bayesian statistical methods have proliferated throughout ecology and evolution. Numerous previous references established both philosophical and computational guidelines for implementing Bayesian methods. However, protocols for incorporating prior information, the defining characteristic of Bayesian philosophy, are nearly nonexistent in the ecological literature. Here, I hope to encourage the use of weakly informative priors in ecology and evolution by providing a ‘consumer\u27s guide’ to weakly informative priors. The first section outlines three reasons why ecologists should abandon noninformative priors: 1) common flat priors are not always noninformative, 2) noninformative priors provide the same result as simpler frequentist methods, and 3) noninformative priors suffer from the same high type I and type M error rates as frequentist methods. The second section provides a guide for implementing informative priors, wherein I detail convenient ‘reference’ prior distributions for common statistical models (i.e. regression, ANOVA, hierarchical models). I then use simulations to visually demonstrate how informative priors influence posterior parameter estimates. With the guidelines provided here, I hope to encourage the use of weakly informative priors for Bayesian analyses in ecology. Ecologists can and should debate the appropriate form of prior information, but should consider weakly informative priors as the new ‘default’ prior for any Bayesian model

    Epigenetic meta-analysis across three civilian cohorts identifies NRG1 and HGS as blood-based biomarkers for post-traumatic stress disorder

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    Aim: Trauma exposure is a necessary, but not deterministic, contributor to post-traumatic stress disorder (PTSD). Epigenetic factors may distinguish between trauma-exposed individuals with versus without PTSD. Materials & methods: We conducted a meta-analysis of PTSD epigenome-wide association studies in trauma-exposed cohorts drawn from civilian contexts. Whole blood-derived DNA methylation levels were analyzed in 545 study participants, drawn from the three civilian cohorts participating in the PTSD working group of the Psychiatric Genomics Consortium. Results: Two CpG sites significantly associated with current PTSD in NRG1 (cg23637605) and in HGS (cg19577098). Conclusion: PTSD is associated with differential methylation, measured in blood, within HGS and NRG1 across three civilian cohorts

    Measuring Novel Antecedents of Mental Illness: The Questionnaire of Unpredictability in Childhood

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    Increasing evidence indicates that, in addition to poverty, maternal depression, and other well-established factors, unpredictability of maternal and environmental signals early in life influences trajectories of brain development, determining risk for subsequent mental illness. However, whereas most risk factors for later vulnerability to mental illness are readily measured using existing, clinically available tools, there are no similar measures for assessing early-life unpredictability. Here we validate the Questionnaire of Unpredictability in Childhood (QUIC) and examine its associations with mental health in the context of other indicators of childhood adversity (e.g., traumatic life events, socioeconomic status, and parenting quality). The QUIC was initially validated through administration to a cohort of adult females (N = 116) and then further refined in two additional independent cohorts (male Veterans, N = 95, and male and female adolescents, N = 175). The QUIC demonstrated excellent internal (α = 0.89) and test–retest reliability (r = 92). Scores on the QUIC were positively correlated with other prospective indicators of exposures to unpredictable maternal inputs in infancy and childhood (unpredictable maternal mood and sensory signals), and accuracy of recall also was confirmed with prospective data. Importantly, the QUIC predicted symptoms of anxiety, depression, and anhedonia in the three study cohorts, and these effects persisted after adjusting for other previously established risk factors. The QUIC, a reliable and valid self-report assessment of exposure to unpredictability in the social, emotional, and physical domains during early life, is a brief, comprehensive, and promising instrument for predicting risk for mental illness

    A monolithic MEMS position sensor for closed-loop high-speed atomic force microscopy

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    The accuracy and repeatability of atomic force microscopy (AFM) imaging significantly depend on the accuracy of the piezoactuator. However, nonlinear properties of piezoactuators can distort the image, necessitating sensor-based closed-loop actuators to achieve high accuracy AFM imaging. The advent of high-speed AFM has made the requirements on the position sensors in such a system even more stringent, requiring higher bandwidths and lower sensor mass than traditional sensors can provide. In this paper, we demonstrate a way for high-speed, high-precision closed-loop AFM nanopositioning using a novel, miniaturized micro-electro-mechanical system position sensor in conjunction with a simple PID controller. The sensor was developed to respond to the need for small, lightweight, high-bandwidth, long-range and sub-nm-resolution position measurements in high-speed AFM applications. We demonstrate the use of this sensor for closed-loop operation of conventional as well as high-speed AFM operation to provide distortion-free images. The presented implementation of this closed-loop approach allows for positioning precision down to 2.1 Å, reduces the integral nonlinearity to below 0.2%, and allows for accurate closed loop imaging at line rates up to 300 Hz

    Spatio-temporal variation in the wintering associations of an alpine bird

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    Many animals make behavioural changes to cope with winter conditions, being gregariousness a common strategy. Several factors have been invoked to explain why gregariousness may evolve during winter, with individuals coming together and separating as they trade off the different costs and benefits of living in groups. These trade-offs may, however, change over space and time as a response to varying environmental conditions. Despite its importance, little is known about the factors triggering gregarious behaviour during winter and its change in response to variation in weather conditions is poorly documented. Here, we aimed at quantifying large-scale patterns in wintering associations over 23 years of the white-winged snowfinch Montifringilla nivalis nivalis. We found that individuals gather in larger groups at sites with harsh wintering conditions. Individuals at colder sites reunite later and separate earlier in the season than at warmer sites. However, the magnitude and phenology of wintering associations are ruled by changes in weather conditions. When the temperature increased or the levels of precipitation decreased, group size substantially decreased, and individuals stayed united in groups for a shorter time. These results shed light on factors driving gregariousness and points to shifting winter climate as an important factor influencing this behaviour

    Male age and its association with reproductive traits in captive and wild house sparrows

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    Evolutionary theory predicts that females seek extra‐pair fertilizations from high‐quality males. In socially monogamous bird species, it is often old males that are most successful in extra‐pair fertilizations. Adaptive models of female extra‐pair mate choice suggest that old males may produce offspring of higher genetic quality than young males because they have proven their survivability. However, old males are also more likely to show signs of reproductive senescence, such as reduced sperm quality. To better understand why old males account for a disproportionally large number of extra‐pair offspring and what the consequences of mating with old males are, we compared several sperm traits of both captive and wild house sparrows, Passer domesticus. Sperm morphological traits and cloacal protuberance volume (a proxy for sperm load) of old and young males did not differ substantially. However, old males delivered almost three times more sperm to the female's egg than young males. We discuss the possibility of a post‐copulatory advantage for old over young males and the consequences for females mated with old males
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